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ISBN: 9781617295645 | 496 pages | 13 Mb

• Graph-Powered Machine Learning
• Page: 496
• Format: pdf, ePub, fb2, mobi
• ISBN: 9781617295645
• Publisher: Manning

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Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs

Graph Powered Machine Learning - Ai+ Training
Machine Learning and Graph Processing (e.g., Knowledge Graphs) have been two of the main trends over the past years. Many powerful Machine Learning 
Graph-Powered Machine Learning (Paperback) - Crow
In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project. Graphs in big data platforms
Graph-Powered Machine Learning - Goodreads
See a Problem? · Preview — Graph-Powered Machine Learning by Alessandro Negro 
Graph-Powered Machine Learning (Paperback) - Francie
Sep 28, 2021 — In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project. Graphs in big data platformsPublisher: ManningPublication Date: September 28th, 2021
Graph-Powered Machine Learning | Book by Alessandro Nego
Graph-Powered Machine Learning by Alessandro Nego - Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and 
Managing Data Sources in Machine Learning - Manning
Take 40% off Graph-Powered Machine Learning by entering fccnegro into the discount code box at checkout at manning.com.
Graph Powered Machine Learning | Meetup
Welcome to our first meetup in 2022! This first event will focus on one of the latest trends in Analytics and Machine Learning: Graph powered 
Graph-Powered Machine Learning - Barnes & Noble
Sep 28, 2021 — Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Graph-Powered Publisher: ManningProduct dimensions: 7.38(w) x 9.25(h) x 0.60(d)
Graph-Powered Machine Learning - Neo4j
This book explores the new way of looking at machine learning – through the lens of graph technology. In particular, this excerpt takes a closer look at how 
Graph-Powered Machine Learning (Paperback) - the river's
Description. Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.
Graph-Powered Machine Learning [Book] - O'Reilly Media
Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine 
1 Machine Learning and Graph: An introduction
If you are an advanced developer interested in exploring the power of graphs, this book could help you to discover a new perspective in the role of the graph 
Graph-Powered Machine Learning 9781617295645
Graph-Powered Machine Learning 9781617295645. 475 81 24MB. English Year 2021. Report DMCA / Copyright. DOWNLOAD FILE.
3 Graphs in Machine Learning Application
How to handle huge amount of data (big data) using a graph data model. This chapter will walk you through how to combine the power of the graph model as a way 

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